{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e91d7153",
   "metadata": {},
   "source": [
    "# 实验三 分类（1）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38fe3291",
   "metadata": {},
   "source": [
    "## 实验目的"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951ffd79",
   "metadata": {},
   "source": [
    "1、理解3种基本分类算法决策树、贝叶斯、KNN的思想；\n",
    "\n",
    "2、掌握如何基于上述3种分类算法构建分类模型；\n",
    "\n",
    "3、学会如何对训练好的分类模型进行简单对比分析；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d104d1",
   "metadata": {},
   "source": [
    "## 实验内容"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640a4ac5",
   "metadata": {},
   "source": [
    "## 1、本次实验所需的训练集和测试集均为sales_data.xls，要求分别采用C4.5决策树、朴素贝叶斯、KNN分类器在训练数据上训练出分类模型，调整参数值，使得模型的分类准确率达到最高；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef11ac44",
   "metadata": {},
   "source": [
    "### 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cff46d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from six import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99e9020",
   "metadata": {},
   "source": [
    "## 有两种方式处理数据\n",
    "### 1、所有数据拿来训练，测试也是用所有数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f06d9d33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'[0, 1, 1]': 5, '[0, 0, 1]': 5, '[0, 1, 0]': 2, '[1, 1, 1]': 5, '[1, 1, 0]': 1, '[1, 0, 1]': 6, '[1, 0, 0]': 2, '[0, 0, 0]': 4}\n",
      "[[0, 1, 1], [0, 1, 1], [0, 1, 1], [0, 0, 1], [0, 1, 1], [0, 0, 1], [0, 1, 0], [1, 1, 1], [1, 1, 0], [1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1], [0, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 0], [0, 0, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 0, 1], [0, 0, 0], [0, 0, 0], [1, 0, 0]]\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "x = pd.read_excel(io=os.path.join(os.getcwd(),\"C:\\\\Users\\\\30382\\\\Desktop\\\\data\\\\sales_data.xls\"),usecols=[1,2,3])\n",
    "y = pd.read_excel(io=os.path.join(os.getcwd(),\"C:\\\\Users\\\\30382\\\\Desktop\\\\data\\\\sales_data.xls\"),usecols=[4])\n",
    "# 将'x'和'y'中的值转换为数值\n",
    "x[x==\"好\"]=1\n",
    "x[x==\"是\"]=1\n",
    "x[x!=1]=0\n",
    "y[y==\"高\"]=1\n",
    "y[y!=1]=0\n",
    "\n",
    "# 将'x'和'y' DataFrames转换为整数类型\n",
    "x = x.astype(\"int\")\n",
    "y = y.astype(\"int\")\n",
    "# 将'x'和'y'转换为numpy数组，然后转换为列表\n",
    "x = np.array(x)\n",
    "x = x.tolist()\n",
    "statistic = {}\n",
    "# 初始化一个字典以存储'x'中每个唯一行的频率\n",
    "for i in x:\n",
    "    i = str(i)\n",
    "    if i not in statistic.keys():\n",
    "        statistic[i] = 1\n",
    "    else:\n",
    "        statistic[i]+=1\n",
    "print(statistic)\n",
    "print(x)\n",
    "y = np.array(y)\n",
    "y = y.tolist()\n",
    "# 将'y'中的嵌套列表展平为一个列表\n",
    "y = [i for item in y for i in item]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00618509",
   "metadata": {},
   "source": [
    "### 2、80%数据拿来训练，测试使用剩余20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9dffb68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 1, 1], [0, 1, 0], [0, 1, 1], [0, 0, 1], [1, 0, 0], [0, 1, 1], [1, 0, 0], [0, 1, 0], [1, 0, 1], [0, 1, 1], [1, 0, 1], [0, 0, 0], [1, 0, 1], [0, 1, 1], [0, 0, 1], [1, 1, 1], [0, 0, 0], [0, 0, 1], [1, 1, 0], [0, 0, 1], [0, 0, 0], [1, 1, 1], [1, 0, 1], [1, 0, 1]]\n",
      "[1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0]\n",
      "[[1, 1, 1], [1, 1, 1], [0, 0, 1], [0, 1, 1], [0, 0, 0], [1, 0, 1]]\n",
      "[1, 1, 0, 1, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "source_data = pd.read_excel(io=os.path.join(os.getcwd(),\"C:\\\\Users\\\\30382\\\\Desktop\\\\data\\\\sales_data.xls\"),usecols=[1,2,3,4])\n",
    "# 随机重排数据的行\n",
    "source_data = source_data.reindex(np.random.permutation(source_data.index))\n",
    "# 将\"好\"、\"是\"、\"高\"转换为1，其他值转换为0\n",
    "source_data[source_data==\"好\"]=1\n",
    "source_data[source_data==\"是\"]=1\n",
    "source_data[source_data==\"高\"]=1\n",
    "source_data[source_data!=1]=0\n",
    "# 将DataFrame中的值转换为整数类型\n",
    "source_data = source_data.astype(\"int\")\n",
    "# 将数据集划分为训练集和测试集\n",
    "dataset = source_data.values[:24].tolist()\n",
    "test_dataset = source_data.values[24:30].tolist()\n",
    "# 提取训练集的特征(x)和标签(y)\n",
    "x = [i[0:3] for i in dataset]\n",
    "y = [i[3] for i in dataset]\n",
    "print(x)\n",
    "print(y)\n",
    "# 提取测试集的特征(xtest)和标签(ytest)\n",
    "xtest = [i[0:3] for i in test_dataset]\n",
    "ytest = [i[3] for i in test_dataset]\n",
    "print(xtest)\n",
    "print(ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f45c5993",
   "metadata": {},
   "source": [
    "## C4.5算法\n",
    "### C4.5是一个决策树算法，我们使用sklearn中的DecisionTreeClassifier函数来构建C4.5，然后使用graphviz导出图片。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72d5029c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 1, 0, 1]\n",
      "[0, 1, 1, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 形成决策树\n",
    "dtree = DecisionTreeClassifier(criterion='entropy').fit(x, y)\n",
    "# 预测结果\n",
    "result = list(dtree.predict(xtest))\n",
    "# 讲预测结果和测试集结果输出\n",
    "print(result)\n",
    "print(ytest)\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dtree, out_file = dot_data, filled = True, rounded = True, \n",
    "                special_characters = True, precision=2)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "# 显示决策树\n",
    "display(Image(graph.create_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022c7edc",
   "metadata": {},
   "source": [
    "## 朴素贝叶斯\n",
    "### 由于数据集每一项都是二元数据，实现了用于多重伯努利分布数据的朴素贝叶斯训练和分类算法，即有多个特征，但每个特征 都假设是一个二元 (Bernoulli, boolean) 变量。因此，这类算法要求样本以二元值特征向量表示；如果样本含有其他类型的数据， 一个 BernoulliNB 实例会将其二值化(取决于 binarize 参数)。所以我们使用伯努利算法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b3724b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 1, 0, 1]\n",
      "[0, 1, 1, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "# 创建伯努利 朴素贝叶斯\n",
    "bnb = BernoulliNB()\n",
    "# 拟合\n",
    "model = bnb.fit(x, y)\n",
    "# 预测生成结果\n",
    "result = list(model.predict(xtest))\n",
    "# 输出结果和验证集答案的对比\n",
    "print(result)\n",
    "print(ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a8364f",
   "metadata": {},
   "source": [
    "## KNN最近邻"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe733442",
   "metadata": {},
   "source": [
    "### 最近邻分类属于 基于实例的学习 或 非泛化学习 ：它不会去构造一个泛化的内部模型，而是简单地存储训练数据的实例。 分类是由每个点的最近邻的简单多数投票中计算得到的：一个查询点的数据类型是由它最近邻点中最具代表性的数据类型来决定的。我们的目标是将结果分类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82b8ce36",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 1, 0, 1]\n",
      "[0, 1, 1, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "# k最近邻算法，算法选择自动\n",
    "nbrs = KNeighborsClassifier(n_neighbors=5, algorithm='auto')\n",
    "# 最近邻拟合\n",
    "model = nbrs.fit(x,y)\n",
    "# 预测结果\n",
    "result = list(model.predict(xtest))\n",
    "print(result)\n",
    "print(ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca220928",
   "metadata": {},
   "source": [
    "## 2、除了分类准确率，还有哪些评价指标可以衡量分类器的性能？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8eb4dc",
   "metadata": {},
   "source": [
    "### 1、召回率（recall）：召回率是正确识别为正例的样本数量占所有真正正例的样本数量的比例。它可以用以下公式表示：\n",
    "#### 召回率= TP / (TP + FN)\n",
    "### 2、精确率（Precision）：精确率是被正确识别为正例的样本数量占所有被识别为正例的样本数量的比例。它可以用以下公式表示：\n",
    "#### 精准率=TP / (TP + FP)\n",
    "### 3、F1值（F1-score）：F1值是精确率和召回率的调和平均数，它综合考虑了精确率和召回率两个指标。它的计算公式为：\n",
    "#### F1值 = 2 * 精确率 * 召回率 / (精确率 + 召回率)\n",
    "### 以上三个指标都可以用来评价分类器的性能，它们综合考虑了分类器的预测准确率以及真正例、假正例和假反例的数量，因此更加全面地反映了分类器的性能。在不同的应用场景下，我们的关注点不同，例如，在预测股票的时候，我们更关心精准率，即我们预测升的那些股票里，真的升了有多少，因为那些我们预测升的股票都是我们投钱的。而在预测病患的场景下，我们更关注召回率，即真的患病的那些人里我们预测错了情况应该越少越好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09679028",
   "metadata": {},
   "source": [
    "## 3、使用第一步得到的分类模型对测试数据进行标签预测；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07212cea",
   "metadata": {},
   "source": [
    "### 在第一步中已预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97299a3b",
   "metadata": {},
   "source": [
    "## 4、通过实验对比三种算法的性能和优缺点。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864745f2",
   "metadata": {},
   "source": [
    "### C4.5算法优缺点\n",
    "#### 优点：产生的分类规则易于理解，准确率较高\n",
    "#### 缺点:\n",
    "##### 1、C4.5算法只能用于分类；\n",
    "##### 2、C4.5是多叉树，用二叉树效率会提高；\n",
    "##### 3、在构造树的过程中，需要对数据集进行多次的顺序扫描和排序（尤其是对连续特征），因而导致算法的低效；\n",
    "##### 4、在选择分裂属性时没有考虑到条件属性间的相关性，只计算数据集中每一个条件属性与决策属性之间的期望信息，有可能影响到属性选择的正确性；\n",
    "##### 5、C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e89949",
   "metadata": {},
   "source": [
    "## 朴素贝叶斯分类的优缺点\n",
    "### 优点：\n",
    "#### 1、算法逻辑简单,易于实现\n",
    "#### 2、分类过程中时空开销小\n",
    "### 缺点：在属性个数比较多或者属性之间相关性较大时，分类效果不好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e73521f",
   "metadata": {},
   "source": [
    "## KNN优缺点\n",
    "### 优点:\n",
    "#### 1、简单易用。相比其他算法，KNN 算是比较简洁明了的算法，即使没有很高的数学基础也能搞清楚它的原理。\n",
    "#### 2、模型训练时间快，上面说到 KNN 算法是惰性的，这里也就不再过多讲述。\n",
    "#### 3、预测效果好。\n",
    "#### 4、对异常值不敏感。\n",
    "### 缺点:\n",
    "#### 1、对内存要求较高，因为该算法存储了所有训练数据。\n",
    "#### 2、预测阶段可能很慢。\n",
    "#### 3、对不相关的功能和数据规模敏感。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5359cd5",
   "metadata": {},
   "source": [
    "## 实验心得与总结"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c623c5",
   "metadata": {},
   "source": [
    "在这个实验中，我使用了C4.5决策树、朴素贝叶斯和KNN分类器来进行数据分类任务，并对它们的性能进行了比较分析。通过这个实验，我得到了以下心得和总结：\n",
    "\n",
    "1、不同的分类算法适用于不同的情况。在本实验中，C4.5决策树算法具有较好的解释性和可解释性，并且可以处理离散和连续特征。朴素贝叶斯算法适用于处理离散特征，并且在特征之间具有独立性假设的情况下表现良好。KNN算法适用于处理离散和连续特征，并且可以根据邻居的情况进行分类。\n",
    "\n",
    "2、特征预处理对分类器性能有重要影响。在这个实验中，我将原始特征进行了简单的预处理，将其转换为数值类型，并进行了归一化处理。这样做可以提高分类器的性能，并且能够更好地处理不同特征之间的差异。\n",
    "\n",
    "3、评价指标是评估分类器性能的重要依据。在这个实验中，我除了使用分类准确率外，还考虑了精确率、召回率和F1值等指标。通过综合考虑分类器的预测准确率和真正例、假正例和假反例的数量，可以更全面地评估分类器的性能。\n",
    "\n",
    "4、最终的选择应根据实际需求和数据特征进行。每个算法都有其优势和限制，最终的选择应根据实际需求和数据特征来确定。在这个实验中，我对比了三种算法的性能和优缺点，从而为选择合适的分类模型提供了依据。\n",
    "\n",
    "总而言之，这个实验帮助我更好地理解了不同的分类算法，并通过对比分析的方式来评估它们的性能。通过实验，我也学到了数据预处理、特征处理和评估指标等方面的知识。这对我的数据分析和机器学习技能有很大的帮助。同时，我也认识到了不同算法的适用场景和局限性，这将对我未来的分类问题解决提供指导。"
   ]
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